一种方法是使用Pipeline,SVC(kernel =’precomputed’)并将自定义内核函数包装为sklearn估计器(BaseEstimator和TransformerMixin的子类)).

例如,sklearn包含custom kernel function chi2_kernel(X,Y = None,gamma = 1.0),它计算特征向量X和Y的核矩阵.

该函数采用参数gamma,最好使用交叉验证来设置.

我们可以对这个函数的参数进行网格搜索,如下所示：

from __future__ import print_function

from __future__ import division

import sys

import numpy as np

import sklearn

from sklearn.base import BaseEstimator, TransformerMixin

from sklearn.cross_validation import train_test_split

from sklearn.datasets import load_digits

from sklearn.grid_search import GridSearchCV

from sklearn.metrics import accuracy_score

from sklearn.metrics.pairwise import chi2_kernel

from sklearn.pipeline import Pipeline

from sklearn.svm import SVC

# Wrapper class for the custom kernel chi2_kernel

class Chi2Kernel(BaseEstimator,TransformerMixin):

def __init__(self, gamma=1.0):

super(Chi2Kernel,self).__init__()

self.gamma = gamma

def transform(self, X):

return chi2_kernel(X, self.X_train_, gamma=self.gamma)

def fit(self, X, y=None, **fit_params):

self.X_train_ = X

return self

def main():

print('python: {}'.format(sys.version))

print('numpy: {}'.format(np.__version__))

print('sklearn: {}'.format(sklearn.__version__))

np.random.seed(0)

# Get some data to evaluate

dataset = load_digits()

X = dataset.data

y = dataset.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)

# Create a pipeline where our custom predefined kernel Chi2Kernel

# is run before SVC.

pipe = Pipeline([

('chi2', Chi2Kernel()),

('svm', SVC()),

])

# Set the parameter 'gamma' of our custom kernel by

# using the 'estimator__param' syntax.

cv_params = dict([

('chi2__gamma', 10.0**np.arange(-9,4)),

('svm__kernel', ['precomputed']),

('svm__C', 10.0**np.arange(-2,9)),

])

# Do grid search to get the best parameter value of 'gamma'.

model = GridSearchCV(pipe, cv_params, cv=5, verbose=1, n_jobs=-1)

model.fit(X_train, y_train)

y_pred = model.predict(X_test)

acc_test = accuracy_score(y_test, y_pred)

print("Test accuracy: {}".format(acc_test))

print("Best params:")

print(model.best_params_)

if __name__ == '__main__':

main()

输出：

python: 2.7.3 (default, Dec 18 2014, 19:10:20)

[GCC 4.6.3]

numpy: 1.8.0

sklearn: 0.16.1

Fitting 5 folds for each of 143 candidates, totalling 715 fits

[Parallel(n_jobs=-1)]: Done 1 jobs | elapsed: 0.4s

[Parallel(n_jobs=-1)]: Done 50 jobs | elapsed: 2.7s

[Parallel(n_jobs=-1)]: Done 200 jobs | elapsed: 9.8s

[Parallel(n_jobs=-1)]: Done 450 jobs | elapsed: 21.6s

[Parallel(n_jobs=-1)]: Done 701 out of 715 | elapsed: 34.8s remaining: 0.7s

[Parallel(n_jobs=-1)]: Done 715 out of 715 | elapsed: 35.4s finished

Test accuracy: 0.989898989899

Best params:

{'chi2__gamma': 0.01, 'svm__C': 10.0, 'svm__kernel': 'precomputed'}

在您的情况下,只需将chi2_kernel替换为计算内核矩阵的函数.